2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献_第9页

Multiple Instance Learning for CNN Based Fire Detection and Localization 基于CNN的火灾检测与定位的多实例学习

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909842

M. Aktas, Ali Bayramcavus, Toygar Akgun

引用次数: 6

Video-based Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes 基于拉格朗日动态的拥挤场景视频瓶颈检测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-08-21 DOI: 10.1109/AVSS.2019.8909861

Maik Simon, Markus Küchhold, T. Senst, Erik Bochinski, T. Sikora

引用次数: 2

What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks 使用生成对抗网络进行异常检测的基于周期一致性的短期运动预测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-08-08 DOI: 10.1109/AVSS.2019.8909853

T. Golda, Nils Murzyn, Chengchao Qu, K. Kroschel

{"title":"What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks","authors":"T. Golda, Nils Murzyn, Chengchao Qu, K. Kroschel","doi":"10.1109/AVSS.2019.8909853","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909853","url":null,"abstract":"Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role. Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature. For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications. The focus is on the usage of static camera setups, since this kind of camera is one of the most often used and belongs to the lower price segment. In order to address this task, multiple subtasks are evaluated, including the influence of existing optical flow methods for the incorporation of short-term temporal information, different forms of network setups and losses for GANs, and the use of morphological operations for further performance improvement. With these extension we achieved up to 2.4% better results. Furthermore, the final method reduced the anomaly detection error for GAN based methods by about 42.8%.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124089715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition 一种基于运动信息的骨骼关节序列表示方法，用于三维动作识别

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-07-30 DOI: 10.1109/AVSS.2019.8909840

C. Caetano, Jessica Sena, F. Brémond, J. A. D. Santos, W. R. Schwartz

{"title":"SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition","authors":"C. Caetano, Jessica Sena, F. Brémond, J. A. D. Santos, W. R. Schwartz","doi":"10.1109/AVSS.2019.8909840","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909840","url":null,"abstract":"Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community. Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints, in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix. To further improve such representations, we introduce a novel skeleton image representation to be used as input of Convolutional Neural Networks (CNNs), named SkeleMotion. The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Different temporal scales are employed to compute motion values to aggregate more temporal dynamics to the representation making it able to capture long-range joint interactions involved in actions as well as filtering noisy motion values. Experimental results demonstrate the effectiveness of the proposed representation on 3D action recognition outperforming the state-of-the-art on NTU RGB+D 120 dataset.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122246929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 129

Human Pose Estimation for Real-World Crowded Scenarios 真实世界拥挤场景的人体姿态估计

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-07-16 DOI: 10.1109/AVSS.2019.8909823

T. Golda, Tobias Kalb, Arne Schumann, Jürgen Beyerer

引用次数: 28

Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks 使用序列到序列网络的人类轨迹自动实时异常检测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-07-12 DOI: 10.1109/AVSS.2019.8909844

Giorgos Bouritsas, Stelios Daveas, A. Danelakis, C. Rizogiannis, S. Thomopoulos

引用次数: 17

TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications* TrackNet:用于跟踪运动应用中的高速和微小物体的深度学习网络*

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-07-08 DOI: 10.1109/AVSS.2019.8909871

Yu-Chuan Huang, I-No Liao, Ching-Hsuan Chen, Tsì-Uí İk, Wen-Chih Peng

{"title":"TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications*","authors":"Yu-Chuan Huang, I-No Liao, Ching-Hsuan Chen, Tsì-Uí İk, Wen-Chih Peng","doi":"10.1109/AVSS.2019.8909871","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909871","url":null,"abstract":"Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. It is still challenging to recognize and position a high-speed and tiny ball accurately from an ordinary video. In this paper, we develop a deep learning network, called TrackNet, to track the tennis ball from broadcast videos in which the ball images are small, blurry, and sometimes with afterimage tracks or even invisible. The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames. The network is evaluated on the video of the men's singles final at the 2017 Summer Universiade, which is available on YouTube. The precision, recall, and $F1$ -measure reach 99.7%, 97.3%, and 98.5%, respectively. To prevent overfitting, 9 additional videos are partially labeled together with a subset from the previous dataset to implement 10-fold cross-validation, and the precision, recall, and $F_{1}$ -measure are 95.3%, 75.7%, and 84.3%, respectively. The source code and dataset are available at https://nol.cs.nctu.edu.tw:234/open-source/TrackNet/.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114273664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

Inverse Attention Guided Deep Crowd Counting Network 反向注意引导深度人群计数网络

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-07-02 DOI: 10.1109/AVSS.2019.8909889

Vishwanath A. Sindagi, Vishal M. Patel

引用次数: 26

K-Same-Siamese-GAN: K-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training K-Same- siame - gan:基于生成对抗网络的人脸图像去识别超参数整定和混合精度训练的K-Same算法

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-03-27 DOI: 10.1109/AVSS.2019.8909866

Yi-Lun Pan, Min-Jhih Haung, Kuo-Teng Ding, Ja-Ling Wu, J. Jang

{"title":"K-Same-Siamese-GAN: K-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training","authors":"Yi-Lun Pan, Min-Jhih Haung, Kuo-Teng Ding, Ja-Ling Wu, J. Jang","doi":"10.1109/AVSS.2019.8909866","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909866","url":null,"abstract":"For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, “how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?” becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also Justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115880813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13